One of the related domain of understanding success of books is that of text readability. Some of the early works propose various readability metrics based on simple characteristics of text documents like sentence length, number of syllables per word etc., for example, FOG [1]}, SMOG [2]} and Flesh-Kincaid [3]} metrics. More advanced readability measures based on list of predetermined words are the Lexile measure [4]}, the Fry Short Passage measure [5]} and the Revised Dale-Chall formula [6]}. Recently, there have been several works [7]}, [8]}, [9]}, [10]}, [11]}, [12]} that focus on predicting and measuring readability of texts based on linguistic features. Collins-Thompson and Callan [11]} adopt simple language modeling approach using a modified Naïve Bayes classifier to estimate reading difficulty of texts. Heilman et al [10]} and Schwarm and Ostendorf [9]} use syntactic features apart from language model features to estimate grade level of texts.
Pitler and Nenkova [12]} predicts readability of texts from Wall Street Journal using lexical, syntactic and discourse features. Kate et al. [8]} propose a model using syntactic and language model features to predict readability of natural language documents irrespective of genres whereas Louis [7]} propose a novel text quality metric for readability that considers unique properties of different genres.
